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Postgraduate Education Corner: CONTEMPORARY REVIEWS IN CRITICAL CARE MEDICINE |

Severity Scoring in the Critically IllSeverity Scoring in the Critically Ill: Part 1: Part 1—Interpretation and Accuracy of Outcome Prediction Scoring Systems

Michael J. Breslow, MD; Omar Badawi, PharmD
Author and Funding Information

From the Department of Research and Product Marketing, Philips Healthcare (Drs Breslow and Badawi), and Department of Pharmacy Practice and Science (Dr Badawi), University of Maryland School of Pharmacy, Baltimore, MD.

Correspondence to: Michael J. Breslow, MD, Department of Research and Product Marketing, Philips Healthcare, Ste 1900, 217 E Redwood St, Baltimore, MD 21202; e-mail: michael.breslow@philips.com


Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2012 American College of Chest Physicians


Chest. 2012;141(1):245-252. doi:10.1378/chest.11-0330
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This review examines the use of scoring systems to assess ICU performance. APACHE (Acute Physiology and Chronic Health Evaluation), MPM (mortality probability model), and SAPS (simplified acute physiology score) are the three major ICU scoring systems in use today. Central to all three is the use of physiologic data for severity adjustment. Differences in the size, nature, and time horizon of the data set translate into minor differences in accuracy and difficulty of data abstraction. APACHE IV provides ICU and hospital predictions for mortality and length of stay, whereas MPM and SAPS only provide hospital mortality predictions (although new algorithms generated from MPM data elements may predict ICU length of stay adequately). The primary use of scoring systems is for assessing ICU performance, with the ratio of actual-to-predicted outcomes in the study cohort providing performance comparisons to the reference ICUs. The reliability of scoring system predictions depends on the completeness and accuracy of the abstracted data; accordingly, ICUs must implement robust data quality control processes. CIs of the ratios are inversely related to sample size, and care must be taken to avoid overinterpreting changes in outcomes. ICU structural and process issues also can affect scoring system performance measures. Despite good discrimination and calibration, scoring systems are used in only 10% to 15% of US ICUs. Without ICU performance data, there is little hope of improving quality and reducing costs. Current demands for transparency and computerization of documentation are likely to drive future use of ICU scoring systems.


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